Biological signals may include substantial amounts of noise. For example, muscular activity is a major source of noise ("muscle noise") in ECG signals. Muscle noise usually appears as rapid, wavy deflections that render the ECG difficult to read.
Simple filters may be used to remove the high frequencies associated with muscle noise from an ECG signal. An example of such a filter is a low-pass filter with a cut-off frequency near 25 Hz. Simple filters such as low pass filters may have the unwanted property of distorting the underlying ECG signal, since the frequency content of the underlying ECG signal may overlap that of muscle noise. For example, the QRS complex of the ECG signal, which corresponds electrically to depolarization of the ventricles of the heart, has frequency content that overlaps muscle noise.
Other noise reduction methods seek to avoid the problem of distorting the QRS complex. One such method employs a filter system that changes the cutoff frequency of the filter during the QRS complex of the ECG signal. The system detects the presence of a QRS complex in the ECG signal and switches the filter to a higher cutoff frequency during the QRS complex. The system switches the filter back to a lower cutoff frequency after the QRS complex ends.
Another system processes multiple ECG signals. These signals are highly redundant in that they arise from a single three-dimensional ECG source (i.e., the heart). The system exploits the redundancy by computing coefficients that allow an input signal to be predicted from the other input signals and using the coefficients to compute a predicted input signal. Next, the system generates a compliance factor that indicates the correspondence between the input signal and the corresponding predicted signal for each time sample of the signals. The system then uses the compliance factor in filtering the input signal.
Another system filters an input signal to create a basic signal and a residual signal, where the basic signal corresponds to a low-pass-filtered portion of the input signal and the residual signal corresponds to the remainder of the input signal. The system then uses redundant signals from multiple points to generate parameters that the system uses to build a signal that the system compares to the residual signal to determine a noise index. The system uses the noise index to scale the residual signal. Finally, the system combines the basic signal and the scaled residual signal to form a filtered version of the input signal.